FSelectorRcpp on CRAN
FSelectorRcpp - Rcpp (free of Java/Weka) implementation of FSelector entropy-based feature selection algorithms with a sparse matrix support, has finally arrived on CRAN after a year of development. It is also equipped with a parallel backend.
Big thanks to the main architect: Zygmunt Zawadzki, zstat, and our reviewer: Krzysztof Słomczyński.
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Get started: Motivation, Installation and Quick Workflow
Blog posts history with use cases
- Entropy Based Image Binarization with imager and FSelectorRcpp, Marcin Kosiński
- Venn Diagram Comparison of Boruta, FSelectorRcpp and GLMnet Algorithms, Marcin Kosiński
A simple entropy based feature selection workflow. Information gain is an easy, linear algorithm that computes the entropy of a dependent and explanatory variables, and the conditional entropy of a dependent variable with a respect to each explanatory variable separately. This simple statistic enables to calculate the belief of the distribution of a dependent variable when we only know the distribution of a explanatory variable.
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